--- title: Qwen-Image-EliGen-Poster canonical_url: "https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster" md_url: "https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster.md" repository: DiffSynth-Studio/Qwen-Image-EliGen-Poster chinese_name: "Qwen-Image EliGen 精确分区控制模型-电商海报" last_updated: 2025-09-28 license: "Apache License 2.0" pipeline_tag: text-to-image-synthesis tasks: - text-to-image-synthesis base_model: - Qwen/Qwen-Image - DiffSynth-Studio/Eligen base_model_relation: adapter parameters: 235.9M tensor_type: - BF16 library_name: - pytorch - safetensors frameworks: - Pytorch downloads: 1089 stars: 24 --- # Qwen-Image-EliGen-Poster > Qwen-Image-EliGen-Poster - DiffSynth-Studio 在 ModelScope 开源的模型。Qwen-Image EliGen 精确分区控制模型-电商海报 DiffSynth-Studio/Qwen-Image-EliGen-Poster 是 ModelScope 魔搭社区上的 235.9M 参数text-to-image-synthesis模型，采用 Apache License 2.0 许可，基于 Qwen/Qwen-Image、DiffSynth-Studio/Eligen 构建。 - **Repository**: DiffSynth-Studio/Qwen-Image-EliGen-Poster - **License**: Apache License 2.0 - **Tasks**: text-to-image-synthesis - **Parameters**: 235.9M - **Base model**: Qwen/Qwen-Image, DiffSynth-Studio/Eligen - **Downloads**: 1089 - **Stars**: 24 - **Last updated**: 2025-09-28 Source: https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster --- # Qwen-Image EliGen 精确分区控制模型-电商海报 ![](./title_image.png) ## 模型介绍 本模型由魔搭社区 DiffSynth-Studio 团队与淘天体验设计团队联合研发并开源。 模型基于 [Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) 构建，专为电商海报场景设计，支持精确的分区布局控制。采用 LoRA 架构，用户可通过输入各实体的文本描述及其对应的区域掩码，灵活控制其在海报中的位置与形状。模型基于 [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) 框架训练，在 [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) 基础上，进一步针对海报图像数据进行了微调优化，显著提升海报版式控制能力。 ## 效果展示 |实体控制条件|生成图| |-|-| |![image1](./assets/1_mask.png)|![mask1](./assets/1.png)| |![image1](./assets/2_mask.png)|![mask1](./assets/2.png)| |![image1](./assets/3_mask.png)|![mask1](./assets/3.png)| ## 推理代码 ``` git clone https://github.com/modelscope/DiffSynth-Studio.git cd DiffSynth-Studio pip install -e . ``` ```python from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig import torch from PIL import Image, ImageDraw, ImageFont from modelscope import dataset_snapshot_download, snapshot_download import random def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): # Create a blank image for overlays overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) colors = [ (165, 238, 173, 80), (76, 102, 221, 80), (221, 160, 77, 80), (204, 93, 71, 80), (145, 187, 149, 80), (134, 141, 172, 80), (157, 137, 109, 80), (153, 104, 95, 80), (165, 238, 173, 80), (76, 102, 221, 80), (221, 160, 77, 80), (204, 93, 71, 80), (145, 187, 149, 80), (134, 141, 172, 80), (157, 137, 109, 80), (153, 104, 95, 80), ] # Generate random colors for each mask if use_random_colors: colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] # Font settings try: font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed except IOError: font = ImageFont.load_default(font_size) # Overlay each mask onto the overlay image for mask, mask_prompt, color in zip(masks, mask_prompts, colors): # Convert mask to RGBA mode mask_rgba = mask.convert('RGBA') mask_data = mask_rgba.getdata() new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] mask_rgba.putdata(new_data) # Draw the mask prompt text on the mask draw = ImageDraw.Draw(mask_rgba) mask_bbox = mask.getbbox() # Get the bounding box of the mask text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) # Alpha composite the overlay with this mask overlay = Image.alpha_composite(overlay, mask_rgba) # Composite the overlay onto the original image result = Image.alpha_composite(image.convert('RGBA'), overlay) # Save or display the resulting image result.save(output_path) return result def example(pipe, seeds, example_id, global_prompt, entity_prompts, height=784, width=1280): dataset_snapshot_download( dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/poster/example_{example_id}/*.png" ) masks = [ Image.open(f"./data/examples/eligen/poster/example_{example_id}/{i}.png").convert('RGB').resize((width, height)) for i in range(len(entity_prompts)) ] negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" for seed in seeds: # generate image image = pipe( prompt=global_prompt, cfg_scale=4.0, negative_prompt=negative_prompt, num_inference_steps=40, seed=seed, height=height, width=width, eligen_entity_prompts=entity_prompts, eligen_entity_masks=masks, ) image.save(f"eligen_poster_example_{example_id}_{seed}.png") image = Image.new("RGB", (width, height), (0, 0, 0)) visualize_masks(image, masks, entity_prompts, f"eligen_poster_example_{example_id}_mask_{seed}.png") pipe = QwenImagePipeline.from_pretrained( torch_dtype=torch.bfloat16, device="cuda", model_configs=[ ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), ], tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), ) snapshot_download( "DiffSynth-Studio/Qwen-Image-EliGen-Poster", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-Poster", allow_file_pattern="model.safetensors", ) pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-Poster/model.safetensors") global_prompt = "一张以柔粉紫为背景的海报，左侧有大号粉紫色文字“Qwen-Image EliGen-Poster”，粉紫色椭圆框内白色小字：“图像精确分区控制模型”。右侧有一只小兔子在拆礼物，旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）。背景有一些白云点缀。整体风格卡通可爱，传达节日惊喜的主题。" entity_prompts = ["粉紫色文字“Qwen-Image EliGen-Poster”", "粉紫色椭圆框内白色小字：“图像精确分区控制模型”", "一只小兔子在拆礼物，小兔子旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）"] seed = [42] example(pipe, seed, 1, global_prompt, entity_prompts) ``` ## 引用 如果您觉得我们的工作对您有所帮助，欢迎引用我们的成果。 ``` @article{zhang2025eligen, title={Eligen: Entity-level controlled image generation with regional attention}, author={Zhang, Hong and Duan, Zhongjie and Wang, Xingjun and Chen, Yingda and Zhang, Yu}, journal={arXiv preprint arXiv:2501.01097}, year={2025} } ```